
"""simple docstring"""



import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


a       :						str       		=		logging.get_logger(__name__)

a       :						List[str]       		=		'''▁'''

a       :						Any       		=		{
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

a       :						str       		=		{
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

a       :						Tuple       		=		{
    '''facebook/m2m100_418M''': 1_0_2_4,
}

# fmt: off
a       :						List[str]       		=		{
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}




class 		a_    (			_UpperCAmelCase						):
    a   :					Tuple			     =					VOCAB_FILES_NAMES
    a   :					Tuple			     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a   :					Tuple			     =					PRETRAINED_VOCAB_FILES_MAP
    a   :					Optional[Any]			     =					['input_ids', 'attention_mask']

    a   :					List[int]			     =					[]
    a   :					List[int]			     =					[]







    def __init__(      self      :							Optional[Any]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Optional[Any]=None  ,       __UpperCamelCase      :							Optional[Any]=None  ,       __UpperCamelCase      :							int="<s>"  ,       __UpperCamelCase      :							Union[str, Any]="</s>"  ,       __UpperCamelCase      :							int="</s>"  ,       __UpperCamelCase      :							List[Any]="<pad>"  ,       __UpperCamelCase      :							Union[str, Any]="<unk>"  ,       __UpperCamelCase      :							Dict="m2m100"  ,       __UpperCamelCase      :							Optional[Dict[str, Any]] = None  ,       __UpperCamelCase      :							Union[str, Any]=8  ,       **__UpperCamelCase      :							Optional[int]  ,       )		->None:






        '''simple docstring'''






        _UpperCAmelCase        = {} if sp_model_kwargs is None else sp_model_kwargs

        _UpperCAmelCase        = language_codes
        _UpperCAmelCase        = FAIRSEQ_LANGUAGE_CODES[language_codes]
        _UpperCAmelCase        = {lang_code: f"""__{lang_code}__""" for lang_code in fairseq_language_code}

        _UpperCAmelCase        = kwargs.get("""additional_special_tokens"""  ,       []  )
        kwargs["additional_special_tokens"] += [
            self.get_lang_token(__UpperCamelCase  )
            for lang_code in fairseq_language_code
            if self.get_lang_token(__UpperCamelCase  ) not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            src_lang=__UpperCamelCase  ,       tgt_lang=__UpperCamelCase  ,       bos_token=__UpperCamelCase  ,       eos_token=__UpperCamelCase  ,       sep_token=__UpperCamelCase  ,       unk_token=__UpperCamelCase  ,       pad_token=__UpperCamelCase  ,       language_codes=__UpperCamelCase  ,       sp_model_kwargs=self.sp_model_kwargs  ,       num_madeup_words=__UpperCamelCase  ,       **__UpperCamelCase  ,       )

        _UpperCAmelCase        = vocab_file
        _UpperCAmelCase        = load_json(__UpperCamelCase  )
        _UpperCAmelCase        = {v: k for k, v in self.encoder.items()}
        _UpperCAmelCase        = spm_file
        _UpperCAmelCase        = load_spm(__UpperCamelCase  ,       self.sp_model_kwargs  )

        _UpperCAmelCase        = len(self.encoder  )

        _UpperCAmelCase        = {
            self.get_lang_token(__UpperCamelCase  ): self.encoder_size + i for i, lang_code in enumerate(__UpperCamelCase  )
        }
        _UpperCAmelCase        = {lang_code: self.encoder_size + i for i, lang_code in enumerate(__UpperCamelCase  )}
        _UpperCAmelCase        = {v: k for k, v in self.lang_token_to_id.items()}

        _UpperCAmelCase        = src_lang if src_lang is not None else """en"""
        _UpperCAmelCase        = tgt_lang
        _UpperCAmelCase        = self.get_lang_id(self._src_lang  )
        self.set_src_lang_special_tokens(self._src_lang  )

        _UpperCAmelCase        = num_madeup_words







    @property
    def   _snake_case       (      self      :							Optional[int]  )		->int:






        '''simple docstring'''






        return len(self.encoder  ) + len(self.lang_token_to_id  )







    @property
    def   _snake_case       (      self      :							Optional[Any]  )		->str:






        '''simple docstring'''






        return self._src_lang







    @src_lang.setter
    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							str  )		->None:






        '''simple docstring'''






        _UpperCAmelCase        = new_src_lang
        self.set_src_lang_special_tokens(self._src_lang  )







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							str  )		->List[str]:






        '''simple docstring'''






        return self.sp_model.encode(__UpperCamelCase  ,       out_type=__UpperCamelCase  )







    def   _snake_case       (      self      :							Optional[Any]  ,       __UpperCamelCase      :							Optional[Any]  )		->Dict:






        '''simple docstring'''






        if token in self.lang_token_to_id:
            return self.lang_token_to_id[token]
        return self.encoder.get(__UpperCamelCase  ,       self.encoder[self.unk_token]  )







    def   _snake_case       (      self      :							Dict  ,       __UpperCamelCase      :							int  )		->str:






        '''simple docstring'''






        if index in self.id_to_lang_token:
            return self.id_to_lang_token[index]
        return self.decoder.get(__UpperCamelCase  ,       self.unk_token  )







    def   _snake_case       (      self      :							Optional[Any]  ,       __UpperCamelCase      :							Any  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = []
        _UpperCAmelCase        = """"""
        for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                out_string += self.sp_model.decode(__UpperCamelCase  ) + token
                _UpperCAmelCase        = []
            else:
                current_sub_tokens.append(__UpperCamelCase  )
        out_string += self.sp_model.decode(__UpperCamelCase  )
        return out_string.strip()







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							List[int]  ,       __UpperCamelCase      :							Optional[List[int]] = None  ,       __UpperCamelCase      :							bool = False  )		->List[int]:






        '''simple docstring'''






        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__UpperCamelCase  ,       token_ids_a=__UpperCamelCase  ,       already_has_special_tokens=__UpperCamelCase  )

        _UpperCAmelCase        = [1] * len(self.prefix_tokens  )
        _UpperCAmelCase        = [1] * len(self.suffix_tokens  )
        if token_ids_a is None:
            return prefix_ones + ([0] * len(__UpperCamelCase  )) + suffix_ones
        return prefix_ones + ([0] * len(__UpperCamelCase  )) + ([0] * len(__UpperCamelCase  )) + suffix_ones







    def   _snake_case       (      self      :							Any  ,       __UpperCamelCase      :							List[int]  ,       __UpperCamelCase      :							Optional[List[int]] = None  )		->List[int]:






        '''simple docstring'''






        if token_ids_a is None:
            return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







    def   _snake_case       (      self      :							Optional[Any]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = {self.convert_ids_to_tokens(__UpperCamelCase  ): i for i in range(self.vocab_size  )}
        vocab.update(self.added_tokens_encoder  )
        return vocab







    def __getstate__(      self      :							Any  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = self.__dict__.copy()
        _UpperCAmelCase        = None
        return state







    def __setstate__(      self      :							Dict  ,       __UpperCamelCase      :							Dict  )		->None:






        '''simple docstring'''






        _UpperCAmelCase        = d

        # for backward compatibility
        if not hasattr(self  ,       """sp_model_kwargs"""  ):
            _UpperCAmelCase        = {}

        _UpperCAmelCase        = load_spm(self.spm_file  ,       self.sp_model_kwargs  )







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Optional[str] = None  )		->Tuple[str]:






        '''simple docstring'''






        _UpperCAmelCase        = Path(__UpperCamelCase  )
        if not save_dir.is_dir():
            raise OSError(f"""{save_directory} should be a directory"""  )
        _UpperCAmelCase        = save_dir / (
            (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
        )
        _UpperCAmelCase        = save_dir / (
            (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
        )

        save_json(self.encoder  ,       __UpperCamelCase  )

        if os.path.abspath(self.spm_file  ) != os.path.abspath(__UpperCamelCase  ) and os.path.isfile(self.spm_file  ):
            copyfile(self.spm_file  ,       __UpperCamelCase  )
        elif not os.path.isfile(self.spm_file  ):
            with open(__UpperCamelCase  ,       """wb"""  ) as fi:
                _UpperCAmelCase        = self.sp_model.serialized_model_proto()
                fi.write(__UpperCamelCase  )

        return (str(__UpperCamelCase  ), str(__UpperCamelCase  ))







    def   _snake_case       (      self      :							Optional[Any]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							str = "en"  ,       __UpperCamelCase      :							Optional[List[str]] = None  ,       __UpperCamelCase      :							str = "ro"  ,       **__UpperCamelCase      :							int  ,       )		->BatchEncoding:






        '''simple docstring'''






        _UpperCAmelCase        = src_lang
        _UpperCAmelCase        = tgt_lang
        self.set_src_lang_special_tokens(self.src_lang  )
        return super().prepare_seqaseq_batch(__UpperCamelCase  ,       __UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Optional[str]  ,       __UpperCamelCase      :							Optional[str]  ,       **__UpperCamelCase      :							Any  )		->Optional[Any]:






        '''simple docstring'''






        if src_lang is None or tgt_lang is None:
            raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""  )
        _UpperCAmelCase        = src_lang
        _UpperCAmelCase        = self(__UpperCamelCase  ,       add_special_tokens=__UpperCamelCase  ,       **__UpperCamelCase  )
        _UpperCAmelCase        = self.get_lang_id(__UpperCamelCase  )
        _UpperCAmelCase        = tgt_lang_id
        return inputs







    def   _snake_case       (      self      :							Dict  )		->Optional[int]:






        '''simple docstring'''






        self.set_src_lang_special_tokens(self.src_lang  )







    def   _snake_case       (      self      :							Optional[Any]  )		->Union[str, Any]:






        '''simple docstring'''






        self.set_tgt_lang_special_tokens(self.tgt_lang  )







    def   _snake_case       (      self      :							Dict  ,       __UpperCamelCase      :							str  )		->None:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_lang_token(__UpperCamelCase  )
        _UpperCAmelCase        = self.lang_token_to_id[lang_token]
        _UpperCAmelCase        = [self.cur_lang_id]
        _UpperCAmelCase        = [self.eos_token_id]







    def   _snake_case       (      self      :							Optional[Any]  ,       __UpperCamelCase      :							str  )		->None:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_lang_token(__UpperCamelCase  )
        _UpperCAmelCase        = self.lang_token_to_id[lang_token]
        _UpperCAmelCase        = [self.cur_lang_id]
        _UpperCAmelCase        = [self.eos_token_id]







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							str  )		->str:






        '''simple docstring'''






        return self.lang_code_to_token[lang]







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							str  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_lang_token(__UpperCamelCase  )
        return self.lang_token_to_id[lang_token]







def 			_UpperCamelCase						(    _A       ,   _A   )     ->	sentencepiece.SentencePieceProcessor:





    """simple docstring"""

    _UpperCAmelCase        = sentencepiece.SentencePieceProcessor(**_A   )
    spm.Load(str(_A   )   )
    return spm


def 			_UpperCamelCase						(    _A   )     ->	Union[Dict, List]:





    """simple docstring"""

    with open(_A       ,   """r"""   ) as f:
        return json.load(_A   )







def 			_UpperCamelCase						(    _A       ,   _A   )     ->	None:





    """simple docstring"""

    with open(_A       ,   """w"""   ) as f:
        json.dump(_A       ,   _A       ,   indent=2   )

"""simple docstring"""



import cva
import numpy as np




class 		a_    :







    def __init__(      self      :							Optional[Any]  ,       __UpperCamelCase      :							float  ,       __UpperCamelCase      :							int  )		->Dict:






        '''simple docstring'''






        if k in (0.0_4, 0.0_6):
            _UpperCAmelCase        = k
            _UpperCAmelCase        = window_size
        else:
            raise ValueError("""invalid k value"""  )







    def __str__(      self      :							Tuple  )		->str:






        '''simple docstring'''






        return str(self.k  )







    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							str  )		->tuple[cva.Mat, list[list[int]]]:






        '''simple docstring'''






        _UpperCAmelCase        = cva.imread(__UpperCamelCase  ,       0  )
        _UpperCAmelCase     ,_UpperCAmelCase        = img.shape
        _UpperCAmelCase        = []
        _UpperCAmelCase        = img.copy()
        _UpperCAmelCase        = cva.cvtColor(__UpperCamelCase  ,       cva.COLOR_GRAY2RGB  )
        _UpperCAmelCase     ,_UpperCAmelCase        = np.gradient(__UpperCamelCase  )
        _UpperCAmelCase        = dx**2
        _UpperCAmelCase        = dy**2
        _UpperCAmelCase        = dx * dy
        _UpperCAmelCase        = 0.0_4
        _UpperCAmelCase        = self.window_size // 2
        for y in range(__UpperCamelCase  ,       h - offset  ):
            for x in range(__UpperCamelCase  ,       w - offset  ):
                _UpperCAmelCase        = ixx[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _UpperCAmelCase        = iyy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _UpperCAmelCase        = ixy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()

                _UpperCAmelCase        = (wxx * wyy) - (wxy**2)
                _UpperCAmelCase        = wxx + wyy
                _UpperCAmelCase        = det - k * (trace**2)
                # Can change the value
                if r > 0.5:
                    corner_list.append([x, y, r]  )
                    color_img.itemset((y, x, 0)  ,       0  )
                    color_img.itemset((y, x, 1)  ,       0  )
                    color_img.itemset((y, x, 2)  ,       2_55  )
        return color_img, corner_list


if __name__ == "__main__":
 a       :						List[Any]       		=		HarrisCorner(0.04, 3)
 a		,			a       :						List[Any]       		=		edge_detect.detect('''path_to_image''')
 cva.imwrite('''detect.png''', color_img)